Google Applies ‘Peer Group’ Analysis To Get Rid Of Malicious Apps On The Play Store

Google is trying to weed out harmful apps from the Play Store for several years now. Over the years, the tech giant has instated various methods to detect malicious apps and remove them from the platform.

In its latest move, Google has applied machine learning on a wider scale by using the technique known as peer group analysis. This technique compares apps to single out the harmful ones. In its approach, Google specifically looks for the apps that request and send out user data.

By using privacy and security signals, apps that function similarly are grouped together. These groups are known as “peer groups” and are created to analyse the app’s functionality. With these groups, Google also assesses the “adequate boundaries of behaviours that may be considered unsafe or intrusive”.

In a dedicated blog post on the issue, Google’s Security and Privacy teamwrites:

Creating peer groups allows us to calibrate our estimates of users’ expectations and set adequate boundaries of behaviours that may be considered unsafe or intrusive. This process helps detect apps that collect or send sensitive data without a clear need and makes it easier for users to find apps that provide the right functionality and respect their privacy.

For example, most colouring book apps don’t need to know a user’s precise location to function, and this can be established by analysing other colouring book apps. By contrast, mapping and navigation apps need to know a user’s location, and often require GPS sensor access.

Looking for Red Flags

It means that if an app within a peer group asks for user information that other apps in its group don’t then that app would be flagged for further analysis by Google. So, through peer groups, Google looks for red flags and then investigates further before removing the app from the Play Store. The process of grouping similar apps into peer groups could be time-consuming if done by human staff, which is why Google has applied machine-learning to the process to make it faster and more accurate.

We hope this method helps Google in making the Play Store safer for users.